{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T22:04:53Z","timestamp":1730325893468,"version":"3.28.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,9,16]]},"DOI":"10.1145\/3564982.3564990","type":"proceedings-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T23:07:22Z","timestamp":1675120042000},"page":"1-7","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Predicting Students' Performance Using Machine Learning Algorithms"],"prefix":"10.1145","author":[{"given":"Charalampos","family":"Dervenis","sequence":"first","affiliation":[{"name":"Department of Business Administration \/ School of Economics and Business Administration, University of Thessaly, Greece"}]},{"given":"Vasileios","family":"Kyriatzis","sequence":"additional","affiliation":[{"name":"Department of Digital Systems \/ School of Technology, University of Thessaly, Greece"}]},{"given":"Spyros","family":"Stoufis","sequence":"additional","affiliation":[{"name":"Computer Science Department \/ School of Science & Technology, Hellenic Open University, Greece"}]},{"given":"Panos","family":"Fitsilis","sequence":"additional","affiliation":[{"name":"Department of Business Administration \/ School of Economics and Business Administration, University of Thessaly, Greece and \rComputer Science Department \/ School of Science & Technology, Hellenic Open University, Greece"}]}],"member":"320","published-online":{"date-parts":[[2023,1,30]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Learning analytics: The definitions, the processes, and the potential","author":"Lias T. E.","year":"2011","unstructured":"Lias , T. E. , & Elias , T. ( 2011 ). Learning analytics: The definitions, the processes, and the potential . Lias, T. E., & Elias, T. (2011). Learning analytics: The definitions, the processes, and the potential."},{"key":"e_1_3_2_1_2_1","volume-title":"The technology outlook for STEM+ education 2012-2017: an NMC horizon report sector analysis (pp. 1-23)","author":"Johnson L.","year":"2012","unstructured":"Johnson , L. , Brown , S. , Cummins , M. , & Estrada , V. ( 2012 ). The technology outlook for STEM+ education 2012-2017: an NMC horizon report sector analysis (pp. 1-23) . The New Media Consortium . Johnson, L., Brown, S., Cummins, M., & Estrada, V. (2012). The technology outlook for STEM+ education 2012-2017: an NMC horizon report sector analysis (pp. 1-23). The New Media Consortium."},{"key":"e_1_3_2_1_3_1","volume-title":"What are learning analytics? eLearnspace. Recuperado de http:\/\/www. elearnspace. org\/blog\/2010\/08\/25\/what-are-learning-analytics\/. Consultado el, 1(07)","author":"Siemens G.","year":"2010","unstructured":"Siemens , G. ( 2010 ). What are learning analytics? eLearnspace. Recuperado de http:\/\/www. elearnspace. org\/blog\/2010\/08\/25\/what-are-learning-analytics\/. Consultado el, 1(07) , 2012. Siemens, G. (2010). What are learning analytics? eLearnspace. Recuperado de http:\/\/www. elearnspace. org\/blog\/2010\/08\/25\/what-are-learning-analytics\/. Consultado el, 1(07), 2012."},{"key":"e_1_3_2_1_4_1","volume-title":"International Computer Assisted Assessment Conference (pp. 79-87)","author":"Greller W.","year":"2014","unstructured":"Greller , W. , Ebner , M. , & Sch\u00f6n , M. ( 2014 , June). Learning analytics: From theory to practice\u2013data support for learning and teaching . In International Computer Assisted Assessment Conference (pp. 79-87) . Springer, Cham. Greller, W., Ebner, M., & Sch\u00f6n, M. (2014, June). Learning analytics: From theory to practice\u2013data support for learning and teaching. In International Computer Assisted Assessment Conference (pp. 79-87). Springer, Cham."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.18608\/jla.2015.22.2"},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8759-8768)","author":"Liu S.","year":"2018","unstructured":"Liu , S. , Qi , L. , Qin , H. , Shi , J. , & Jia , J. ( 2018 ). Path aggregation network for instance segmentation . In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8759-8768) . Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018). Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8759-8768)."},{"issue":"1","key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.14786\/flr.v1i1.13","article-title":"Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks","volume":"1","author":"Musso M. F.","year":"2013","unstructured":"Musso , M. F. , Kyndt , E. , Cascallar , E. C. , & Dochy , F. ( 2013 ). Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks . Frontline Learning Research , 1 ( 1 ), 42 - 71 . Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2013). Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks. Frontline Learning Research, 1(1), 42-71.","journal-title":"Frontline Learning Research"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-011-9234-x"},{"key":"e_1_3_2_1_9_1","volume-title":"Predicting student performance from combined data sources. In\u00a0Educational data mining\u00a0(pp. 175-202)","author":"Wolff A.","year":"2014","unstructured":"Wolff , A. , Zdrahal , Z. , Herrmannova , D. , & Knoth , P. ( 2014 ). Predicting student performance from combined data sources. In\u00a0Educational data mining\u00a0(pp. 175-202) . Springer , Cham . Wolff, A., Zdrahal, Z., Herrmannova, D., & Knoth, P. (2014). Predicting student performance from combined data sources. In\u00a0Educational data mining\u00a0(pp. 175-202). Springer, Cham."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","DOI":"10.1561\/9781601982957","volume-title":"Learning deep architectures for AI. Foundations and trends\u00ae in Machine Learning, 2(1), 1-127","author":"Bengio Y.","year":"2009","unstructured":"Bengio , Y. ( 2009 ). Learning deep architectures for AI. Foundations and trends\u00ae in Machine Learning, 2(1), 1-127 . Bengio, Y. (2009). Learning deep architectures for AI. Foundations and trends\u00ae in Machine Learning, 2(1), 1-127."},{"key":"e_1_3_2_1_11_1","volume-title":"Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, 4(4), 253-278","author":"Corbett A. T.","year":"1994","unstructured":"Corbett , A. T. , & Anderson , J. R. ( 1994 ). Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, 4(4), 253-278 . Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, 4(4), 253-278."},{"key":"e_1_3_2_1_12_1","volume-title":"International educational data mining society.","author":"Liu R.","year":"2015","unstructured":"Liu , R. , & Koedinger , K. R. ( 2015 ). Variations in Learning Rate: Student Classification Based on Systematic Residual Error Patterns across Practice Opportunities . International educational data mining society. Liu, R., & Koedinger, K. R. (2015). Variations in Learning Rate: Student Classification Based on Systematic Residual Error Patterns across Practice Opportunities. International educational data mining society."},{"key":"e_1_3_2_1_13_1","volume-title":"ASCILITE-Australian Society for Computers in Learning in Tertiary Education Annual Conference (pp. 68-72)","author":"Atif A.","year":"2013","unstructured":"Atif , A. , Richards , D. , Bilgin , A. , & Marrone , M. ( 2013 ). Learning analytics in higher education: a summary of tools and approaches . In ASCILITE-Australian Society for Computers in Learning in Tertiary Education Annual Conference (pp. 68-72) . Australasian Society for Computers in Learning in Tertiary Education. Atif, A., Richards, D., Bilgin, A., & Marrone, M. (2013). Learning analytics in higher education: a summary of tools and approaches. In ASCILITE-Australian Society for Computers in Learning in Tertiary Education Annual Conference (pp. 68-72). Australasian Society for Computers in Learning in Tertiary Education."},{"key":"e_1_3_2_1_14_1","volume-title":"A two-phase machine learning approach for predicting student outcomes.\u00a0Education and Information Technologies,\u00a026(1), 69-88","author":"Iatrellis O.","year":"2021","unstructured":"Iatrellis , O. , Savvas , I. K. , Fitsilis , P. , & Gerogiannis , V. C. ( 2021 ). A two-phase machine learning approach for predicting student outcomes.\u00a0Education and Information Technologies,\u00a026(1), 69-88 . Iatrellis, O., Savvas, I. K., Fitsilis, P., & Gerogiannis, V. C. (2021). A two-phase machine learning approach for predicting student outcomes.\u00a0Education and Information Technologies,\u00a026(1), 69-88."},{"key":"e_1_3_2_1_15_1","volume-title":"Data mining in education.\u00a0Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,\u00a03(1), 12-27","author":"Romero C.","year":"2013","unstructured":"Romero , C. , & Ventura , S. ( 2013 ). Data mining in education.\u00a0Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,\u00a03(1), 12-27 . Romero, C., & Ventura, S. (2013). Data mining in education.\u00a0Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,\u00a03(1), 12-27."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1355"},{"key":"e_1_3_2_1_17_1","volume-title":"Using data mining to predict secondary school student performance","author":"Cortez P.","year":"2008","unstructured":"Cortez , P. , & Silva , A. M. G. ( 2008 ). Using data mining to predict secondary school student performance . Cortez, P., & Silva, A. M. G. (2008). Using data mining to predict secondary school student performance."},{"key":"e_1_3_2_1_18_1","volume-title":"\u00a0Orange: Data Mining Toolbox in Python,\u00a0Journal of Machine Learning Research\u00a014(Aug): 2349\u22122353","author":"Demsar J","year":"2013","unstructured":"Demsar J , Curk T , Erjavec A , Gorup C , Hocevar T , Milutinovic M , Mozina M , Polajnar M , Toplak M , Staric A , Stajdohar M , Umek L , Zagar L , Zbontar J , Zitnik M , Zupan B ( 2013 ) \u00a0Orange: Data Mining Toolbox in Python,\u00a0Journal of Machine Learning Research\u00a014(Aug): 2349\u22122353 Demsar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, Mozina M, Polajnar M, Toplak M, Staric A, Stajdohar M, Umek L, Zagar L, Zbontar J, Zitnik M, Zupan B (2013)\u00a0Orange: Data Mining Toolbox in Python,\u00a0Journal of Machine Learning Research\u00a014(Aug): 2349\u22122353"},{"key":"e_1_3_2_1_19_1","volume-title":"Learning k for knn classification.\u00a0ACM Transactions on Intelligent Systems and Technology (TIST),\u00a08(3), 1-19","author":"Zhang S.","year":"2017","unstructured":"Zhang , S. , Li , X. , Zong , M. , Zhu , X. , & Cheng , D. ( 2017 ). Learning k for knn classification.\u00a0ACM Transactions on Intelligent Systems and Technology (TIST),\u00a08(3), 1-19 . Zhang, S., Li, X., Zong, M., Zhu, X., & Cheng, D. (2017). Learning k for knn classification.\u00a0ACM Transactions on Intelligent Systems and Technology (TIST),\u00a08(3), 1-19."},{"key":"e_1_3_2_1_20_1","volume-title":"K-nearest neighbour classifiers-a tutorial.\u00a0ACM Computing Surveys (CSUR),\u00a054(6), 1-25","author":"Cunningham P.","year":"2021","unstructured":"Cunningham , P. , & Delany , S. J. ( 2021 ). K-nearest neighbour classifiers-a tutorial.\u00a0ACM Computing Surveys (CSUR),\u00a054(6), 1-25 . Cunningham, P., & Delany, S. J. (2021). K-nearest neighbour classifiers-a tutorial.\u00a0ACM Computing Surveys (CSUR),\u00a054(6), 1-25."},{"key":"e_1_3_2_1_21_1","volume-title":"A user's guide to support vector machines. In\u00a0Data mining techniques for the life sciences\u00a0(pp. 223-239)","author":"Ben-Hur A.","year":"2010","unstructured":"Ben-Hur , A. , & Weston , J. ( 2010 ). A user's guide to support vector machines. In\u00a0Data mining techniques for the life sciences\u00a0(pp. 223-239) . Humana Press . Ben-Hur, A., & Weston, J. (2010). A user's guide to support vector machines. In\u00a0Data mining techniques for the life sciences\u00a0(pp. 223-239). Humana Press."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_3_2_1_23_1","volume-title":"Selecting training sets for support vector machines: a review.\u00a0Artificial Intelligence Review,\u00a052(2), 857-900","author":"Nalepa J.","year":"2019","unstructured":"Nalepa , J. , & Kawulok , M. ( 2019 ). Selecting training sets for support vector machines: a review.\u00a0Artificial Intelligence Review,\u00a052(2), 857-900 . Nalepa, J., & Kawulok, M. (2019). Selecting training sets for support vector machines: a review.\u00a0Artificial Intelligence Review,\u00a052(2), 857-900."},{"key":"e_1_3_2_1_24_1","volume-title":"A survey of random forest based methods for intrusion detection systems.\u00a0ACM Computing Surveys (CSUR),\u00a051(3), 1-36","author":"Resende P. A. A.","year":"2018","unstructured":"Resende , P. A. A. , & Drummond , A. C. ( 2018 ). A survey of random forest based methods for intrusion detection systems.\u00a0ACM Computing Surveys (CSUR),\u00a051(3), 1-36 . Resende, P. A. A., & Drummond, A. C. (2018). A survey of random forest based methods for intrusion detection systems.\u00a0ACM Computing Surveys (CSUR),\u00a051(3), 1-36."},{"key":"e_1_3_2_1_25_1","volume-title":"Random forests.\u00a0Machine learning,\u00a045(1), 5-32","author":"Breiman L.","year":"2001","unstructured":"Breiman , L. ( 2001 ). Random forests.\u00a0Machine learning,\u00a045(1), 5-32 . Breiman, L. (2001). Random forests.\u00a0Machine learning,\u00a045(1), 5-32."},{"key":"e_1_3_2_1_26_1","volume-title":"Introduction to support vector machines and kernel methods.\u00a0publication at https:\/\/www. researchgate. net\/publication\/332370436","author":"Ashfaque J. M.","year":"2019","unstructured":"Ashfaque , J. M. , & Iqbal , A. ( 2019 ). Introduction to support vector machines and kernel methods.\u00a0publication at https:\/\/www. researchgate. net\/publication\/332370436 . Ashfaque, J. M., & Iqbal, A. (2019). Introduction to support vector machines and kernel methods.\u00a0publication at https:\/\/www. researchgate. net\/publication\/332370436."}],"event":{"name":"ICACS 2022: 2022 The 6th International Conference on Algorithms, Computing and Systems","acronym":"ICACS 2022","location":"Larissa Greece"},"container-title":["Proceedings of the 6th International Conference on Algorithms, Computing and Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3564982.3564990","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T23:08:28Z","timestamp":1675120108000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3564982.3564990"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,16]]},"references-count":26,"alternative-id":["10.1145\/3564982.3564990","10.1145\/3564982"],"URL":"https:\/\/doi.org\/10.1145\/3564982.3564990","relation":{},"subject":[],"published":{"date-parts":[[2022,9,16]]},"assertion":[{"value":"2023-01-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}